Improving data quality of a trauma register

2017 ◽  
Author(s):  
Estefania Rabaneda Romero
Keyword(s):  
Data Quality

Designing Information Product (IP) Maps On the Process of Data Processing and Academic Information

2017 ◽  
Vol 4 (1) ◽  
pp. 25-31 ◽  
Author(s):  
Diana Effendi
Keyword(s):  
Data Quality ◽  
Data Management ◽  
Information Management ◽  
Information Quality ◽  
Quality Data ◽  
Management Approach ◽  
Quality Of Data ◽  
Information Product ◽  
Academic Activities

Information Product Approach (IP Approach) is an information management approach. It can be used to manage product information and data quality analysis. IP-Map can be used by organizations to facilitate the management of knowledge in collecting, storing, maintaining, and using the data in an organized. The  process of data management of academic activities in X University has not yet used the IP approach. X University has not given attention to the management of information quality of its. During this time X University just concern to system applications used to support the automation of data management in the process of academic activities. IP-Map that made in this paper can be used as a basis for analyzing the quality of data and information. By the IP-MAP, X University is expected to know which parts of the process that need improvement in the quality of data and information management.   Index term: IP Approach, IP-Map, information quality, data quality. REFERENCES[1] H. Zhu, S. Madnick, Y. Lee, and R. Wang, “Data and Information Quality Research: Its Evolution and Future,” Working Paper, MIT, USA, 2012.[2] Lee, Yang W; at al, Journey To Data Quality, MIT Press: Cambridge, 2006.[3] L. Al-Hakim, Information Quality Management: Theory and Applications. Idea Group Inc (IGI), 2007.[4] “Access : A semiotic information quality framework: development and comparative analysis : Journal ofInformation Technology.” [Online]. Available: http://www.palgravejournals.com/jit/journal/v20/n2/full/2000038a.html. [Accessed: 18-Sep-2015].[5] Effendi, Diana, Pengukuran Dan Perbaikan Kualitas Data Dan Informasi Di Perguruan Tinggi MenggunakanCALDEA Dan EVAMECAL (Studi Kasus X University), Proceeding Seminar Nasional RESASTEK, 2012, pp.TIG.1-TI-G.6.

scholarly journals Response Behavior and Quality of Survey Data: Comparing Elderly Respondents in Institutions and Private Households

2021 ◽  
pp. 004912412199553
Author(s):  
Jan-Lucas Schanze
Keyword(s):  
Data Quality ◽  
The Elderly ◽  
Response Behavior ◽  
Quality Of Data ◽  
Social Surveys ◽  
Private Households ◽  
Confounding Variables ◽  
Health Related ◽  
Survey Interviews

An increasing age of respondents and cognitive impairment are usual suspects for increasing difficulties in survey interviews and a decreasing data quality. This is why survey researchers tend to label residents in retirement and nursing homes as hard-to-interview and exclude them from most social surveys. In this article, I examine to what extent this label is justified and whether quality of data collected among residents in institutions for the elderly really differs from data collected within private households. For this purpose, I analyze the response behavior and quality indicators in three waves of Survey of Health, Ageing and Retirement in Europe. To control for confounding variables, I use propensity score matching to identify respondents in private households who share similar characteristics with institutionalized residents. My results confirm that most indicators of response behavior and data quality are worse in institutions compared to private households. However, when controlling for sociodemographic and health-related variables, differences get very small. These results suggest the importance of health for the data quality irrespective of the housing situation.

scholarly journals Thinking about police data: Analysts’ perceptions of data quality in Canadian policing

2021 ◽  
pp. 0032258X2110214
Author(s):  
Christopher D O’Connor ◽  
John Ng ◽  
Dallas Hill ◽  
Tyler Frederick
Keyword(s):  
Big Data ◽  
Data Collection ◽  
Data Quality ◽  
Research Culture ◽  
Police Services ◽  
Police Data ◽  
Data Collection And Analysis ◽  
Quality Issues

Policing is increasingly being shaped by data collection and analysis. However, we still know little about the quality of the data police services acquire and utilize. Drawing on a survey of analysts from across Canada, this article examines several data collection, analysis, and quality issues. We argue that as we move towards an era of big data policing it is imperative that police services pay more attention to the quality of the data they collect. We conclude by discussing the implications of ignoring data quality issues and the need to develop a more robust research culture in policing.

Research on the Validation Method of Product Data Quality

2014 ◽  
Vol 989-994 ◽  
pp. 1631-1634
Author(s):  
Ping Wang ◽  
Bin Wang
Keyword(s):  
Data Quality ◽  
High Efficiency ◽  
Engineering Analysis ◽  
Validation Process ◽  
Software Environment ◽  
Product Data ◽  
Validation Method ◽  
Source Data ◽  
Cnc Programming

Product data is the source data of product lifecycle in manufacturing enterprise. The quality of product data largely determines the effect of the application of Engineering analysis, simulation assembly and CNC programming work and so on. In order to solve the problems of the existing product data quality, such as validation custom trival, lack of high efficiency and flexibility, etc. The validation method of product data quality (PDQ) based on class was proposed in NX software environment, the representation of validation rules classes of product data quality, validation rules customization and implementation of validation process were introduced in detail in this study. Finally, an application case was employed to verify the practicability and effectiveness of the proposed method.

Data Quality of Longitudinally Collected Patient-Reported Outcomes (PROs) after Thoracic Surgery: A Comparison of Paper- and Web-Based Assessments (Preprint)

2021 ◽  
Author(s):  
Hongfan Yu ◽  
Qingsong Yu ◽  
Yuxian Nie ◽  
Wei Xu ◽  
Yang Pu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Thoracic Surgery ◽  
Data Quality ◽  
Severe Disease ◽  
Sampling Bias ◽  
Patient Reported Outcome ◽  
Web Based ◽  
Quality Of Life Scale ◽  
Patient Reported

BACKGROUND High-frequent patient-reported outcome (PRO) assessments are used to measure patients’ symptoms after surgery for surgical research; however, quality of those longitudinal PRO data has seldom been discussed. OBJECTIVE To describe errors, to identify factors influencing the data quality, and to profile error trajectories of data longitudinally collected via paper-and-pencil (P&P) or web-based-assessment (ePRO) after thoracic surgery. METHODS We extracted longitudinal PRO data from two prospective clinical studies. PROs were assessed by the MD Anderson Symptom Inventory Lung Cancer Module and single-item Quality of Life Scale before surgery and then daily after surgery until discharge or up to 14 days of hospitalization. Patient compliance and data error were identified and compared between P&P and ePRO. Generalized estimating equations models and two-piecewise models were used to describe trajectories of error incidence over time and to identify the risk factors. RESULTS Among 629 patients with at least 2 PRO assessments, 440 completed 3347 P&P assessments and 189 completed 1291 ePRO assessments. In total, 49.44% of patients had at least 1 error, including 1) missing items (64.69%), 2) modifications without signatures (27.99%), 3) selection of multiple options (3.02%), 4) missing patient signatures (2.54%), 5) missing researcher signatures (1.45%) and 6) missing completion dates (0.3%). ePRO patients had fewer errors than P&P patients (30.16% vs. 57.73%, p <0.0001). Compared with ePRO patients, those using P&P were older, less educated and sicker. Common risk factors of having errors were with a lower education level (P&P, OR=1.39, 95%CL=1.20-1.62, p<.0001; ePRO, OR=1.82, 95%CI=1.22-2.72, p=0.0032), treated in a provincial hospital (P&P, OR=3.34, 95%CI=2.10-5.33, p<.0001; ePRO, OR=4.73, 95%CI=2.18-10.25, p<.0001) and with severe disease (P&P, OR=1.63, 95%CI=1.33-1.99, p<.0001; ePRO, OR=2.70, 95%CI=1.53-4.75, p=0.0006). Errors peaked on postoperative day (POD) 1 for P&P, and on POD 2 for ePRO. CONCLUSIONS ePRO might be superior to P&P in terms of data quality. However, sampling bias needs to be considered for studies using longitudinal PROs as major outcomes.

scholarly journals Getting the Numbers Right on China's Actual Overseas Investment: The Case of the Netherlands

2017 ◽  
Vol 46 (1) ◽  
pp. 187-209 ◽  
Author(s):  
Piter De Jong ◽  
Mark J. Greeven ◽  
Haico Ebbers
Keyword(s):  
The Netherlands ◽  
Data Quality ◽  
Chinese Firms ◽  
Chamber Of Commerce ◽  
Overseas Investment ◽  
Data Source

This study assesses the quality of Chinese outbound FDI data. In our case study of the Netherlands, we checked the data quality of the often-used Orbis/Amadeus database and its data source, the Dutch Chamber of Commerce (Kamer van Koophandel, KVK), which has one of the oldest and, arguably, one of the better databases within Europe. We analysed Chinese investments in the Netherlands and show that six adjustments are necessary to clean up the data. We also show that not making these adjustments can significantly impact the outcome of research. The cleaned-up data show that sampled Chinese firms are young, small, and private.

Sign in / Sign up

Export Citation Format

Share Document